PROJECT SUMMARY: OVERALL To respond adaptively to the environment, the brain must process information to develop accurate and stable representations of the current state of the environment (?state representation?). This requires precise neural activity timing synchrony between prefrontal and sensory systems and within prefrontal networks. Our Center focuses on the unifying hypothesis that processes underlying state representation dysfunction are relevant to psychosis, providing a window into pathophysiologic heterogeneity and precision treatment. Four Projects span three species (nonhuman primates, mice, and humans) and eight methodologies (genetic manipulations, slice physiology, ensemble recordings, LFP, behavior, EEG, fMRI, cognitive training). We use a central computational perspective to translate and integrate across species and methodologies: Changes in neural information processing affect parameters underlying attractor dynamics and influence state representation processes. Such changes create observable effects in behavior and neurophysiology, which we can study through the lens of attractor network models to inform our understanding of pathophysiologic heterogeneity, clinical trajectories, and precision treatment. Each Project: 1) Uses the same behavioral tasks to probe components of state representation across species and experiments; 2) Accesses parallel neurophysiologic metrics, with a focus on neural system activity timing, excitatory-inhibitory balance, and noise; 3) Uses advanced data-driven causal discovery analyses to facilitate cross-paradigm integration and novel hypothesis generation. The Projects are supported by a Translational Neurophysiology Core, a Computational Core, and an Administrative Core. Aim 1 investigates behavior and neurophysiology of state representation dysfunctions characteristic of psychosis in a nonhuman primate model of prefrontal network failure in psychosis mediated through NMDA-R signaling (PROJECT 1); in mice with cell type-specific ablation of NMDA-R function and carrying psychosis- associated genetic variants (PROJECT 2); and from an EEG-fMRI study of healthy controls and people with early psychosis (PROJECT 3). Aim 2 develops attractor network models of state representation at multiple levels of detail, incorporating behavioral, synaptic, and cellular microcircuit data from animal neurophysiology studies (PROJECTS 1 & 2) to identify parameters that account for state representation dysfunctions characteristic of psychosis and the behavioral and neurophysiological observations made in humans (PROJECTS 3 & 4). Aims 3 and 4 focus on reliability and predictive significance of state representation dysfunctions in early psychosis, and precision treatment approaches targeting specific dysfunctions.